Automated liquid handling robot for rapid lateral flow assay development

The lateral flow assay (LFA) is one of the most popular technologies on the point-of-care diagnostics market due to its low cost and ease of use, with applications ranging from pregnancy to environmental toxins to infectious disease. While the use of these tests is relatively straightforward, significant development time and effort are required to create tests that are both sensitive and specific. Workflows to guide the LFA development process exist but moving from target selection to an LFA that is ready for field testing can be labor intensive, resource heavy, and time consuming. To reduce the cost and the duration of the LFA development process, we introduce a novel development platform centered on the flexibility, speed, and throughput of an automated robotic liquid handling system. The system comprises LFA-specific hardware and software that enable large optimization experiments with discrete and continuous variables such as antibody pair selection or reagent concentration. Initial validation of the platform was demonstrated during development of a malaria LFA but was readily expanded to encompass development of SARS-CoV-2 and Mycobacterium tuberculosis LFAs. The validity of the platform, where optimization experiments are run directly on LFAs rather than in solution, was based on a direct comparison between the robotic system and a more traditional ELISA-like method. By minimizing hands-on time, maximizing experiment size, and enabling improved reproducibility, the robotic system improved the quality and quantity of LFA assay development efforts. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s00216-022-03897-9.

A. Materials provided with manuscript Alongside our manuscript, we have provided additional materials to enable replication of our work. This includes both software and hardware developed specifically for the application of lateral flow assay (LFA) development. All support files described below are available on GitHub (https://github.com/Robot-LFA). The general workflow of steps during an experiment using this system starts from choosing variables to investigate and ends with result analysis ( Figure S1). Along this workflow, there are 3 major pieces of software developed for this work, the Hamilton Master Method, the worklist generator, and the image analyzer. Information about each piece of code can be found in their respective sections below.

Choose variables to investigate
Design experiment Generate list of commands, using homemade software Perform experiment using the liquid handling robot Obtain data Analyze results, determine better assay design Run the test using the robot Acquire quantitative signal by imaging and image analysis Figure S1. Diagram depicting the steps of the integrated robotic system in an experiment during assay development.
As an example workflow, at the beginning stages for LFA development one of the first steps is to select the antibody binding pair. The choice of capture and detection antibody would be selected as the variables to investigate for this experiment ( Figure S1). During this process, the assay format (e.g. direct sandwich immunoassay, competitive immunoassay, or modified sandwich immunoassay with streptavidin capture) would be an important variable. In addition, at this stage the LFA geometry and specific materials to be used would need to be defined. The exact experimental details, including the specific antigen to be tested, number and types of pipetting steps, number of imaging steps, number of replicates, etc, would then be defined based on the constraints of that specific LFA. The LFA holder would also be chosen here, as the choice will depend on the availability. This is also the time to confirm that the appropriate hardware and liquid class definitions are selected.
From this information, the worklist to be input into the Hamilton Master Method would then be generated using the Python worklist generator code. We recommend running all new worklists in simulation mode before running the full experiment, alongside a small sample test where the method, imaging, and image analysis can be validated. Once any additional tweaks to the worklist have been made, the larger experiment can be performed. For many of our experiments, a single "experiment" could consist of an entire week's worth of robot runs. The images acquired from the robot can then be analyzed using the Python image analysis software to quantify test line intensities, which are then used for result analysis and further experimentation. This is an example workflow for the use of this system, as you can image the number of variables that can be tested on a system such as this are many.

Hamilton Master Method
The Hamilton master method reads the worklist and executes the experiments with proper transfer sources and destinations, liquid classes, time delay, etc. as specified in the worklist. The worklist typically instructs the robot to acquire images as the results of the assays. The master method was written using the Hamilton Venus software and is included in the attached package (ivl_master_method_robot_paper.pkg).
Alongside the master method, for this system to work effectively we made (1) hardware definitions in the Hamilton software specific to the LFA hardware designed for this work (more detail about the hardware itself below), and (2) liquid class definitions for the liquids used for this work. More information about each of these is included below.

Hamilton Hardware definitions
Hardware definitions were designed specifically for the hardware used for this work. This was done within the Hamilton software, specifically for the "deck" where the LFAs are placed during each run. The hardware definitions are also included. For each individual machine, some optimization in house might be required.

Hamilton Liquid Class definitions
Liquid classes were designed specifically for the aqueous reagents used for this LFA, a running buffer containing detergent and whole blood. This was done using the Hamilton Liquid Verification Kit. Depending on the pipetting step, whether dispensing into a well or onto nitrocellulose, a different dispense method was required (Jet empty vs. Surface empty), therefore optimization took place for both. For blood, we chose not to use Jet empty to prevent the generation of aerosols. Below are tables that contain coefficient of variation (CV) for each volume as measured by the new liquid classes. These liquid classes are attached (ivl_liquid_classes.mdb), however due to the nature of liquid handling robots it is recommended to do optimization in house, as small variables in humidity and altitude can have a large impact on pipetting performance. Because of this, we recommend doing in house optimization of the liquid classes on your robotic set up before beginning any experimental work.

Python code to generate worklists
The worklist generator inputs variables to investigate (which solutions, or dispense locations, or dispense volumes) and outputs worklists (list of commands for the robot). The code is in the attached file (robot_worklist_generator.zip). More information on how to run the code can be found in the included README.

Python code to analyze images
The image analyzer finds the regions of interest (signal lines or spots) on the resulting images and quantifies the corresponding signal intensities (robot_image_analyzer.zip). This code can be modified as required by the location of the test and control lines. One important validation step was that we compare the imaging and image analysis process between this new system and our "gold standard" LFA reader that we use for development. LFAs are most commonly read by eye, or with an LFA specific reader, though there has been a push in recent years to use cell phones to read LFAs. The Axxin reader is a commonly used instrument that is both CE approved and FDA ready, and has been used around the world for LFA readouts. Here, we compared the signal readout for strips with a range of antigen concentrations to determine whether the signal we read from the robot correlates with the reads from the Axxin. This data, as shown in Figure S6, shows a linear fit when plotting the Hamilton signal vs. the Axxin signal. This suggests that the signal analysis run on images acquired by the camera on the Hamilton STAR are sufficient for our purposes. 6. Lateral flow assay (LFA) specific hardware As described in the main text, we have developed four different hardware components to enable testing of LFAs on a Hamilton STAR robot. Computer aided design (CAD) files are included in the Supplemental Material (robot_paper_CAD.zip). The labware definitions are included in the master method package (ivl_master_method_robot_paper.pkg).
The first holder is a fully adjustable lateral flow assay holder, as shown in Figure S1. The parts were made by an in-house machine shop. The bottom base was made of stainless steel, and the other parts were made of aluminum. The threaded rod was made of stainless steel (10-32). The nuts for position adjustment were made of plastic. The second lateral flow assay holder was designed to substitute a degree of freedom for adjustment (height) with a set of pre-made 3D printed cross bars ( Figure S4, Figure S5). The user cannot freely adjust the heights of pinch points and wells like with the first holder, but can freely choose different cross bars with different pinch point and well geometries. The cross bars and all other parts, besides the bottom base, were 3D printed using Vero inks on a polyjet printer (Stratasys J750). The aluminum bottom base (to correct for any warping of the 3D printed parts on top) was machined to have 2 sets of 3 threaded holes, located along the 2 long sides of the rectangle, which has the same footprint as the regular well plate. The bottom 3D-printed plate serves as the floor onto which LFAs are laid. The white frame provides teeth along the longer edges with rulers on the side. The clear cross bars containing pinch points or wells have teeth on the 2 ends to register with the teeth on the white frame. The black clamp bars are used to fix the cross bars to the white frame. The attached slide deck (Adjustable_Array_Strip_Cartridge_paper_SI.pdf) explains how this holder is used. Figure S4. LFA strip array holder with laterally movable features (e.g. pinch points and wells. A) Cartoon of an assembled LFA strip array holder. B) Photograph of a partially assembled holder, without clamps. The inset shows the side ruler. C) Photograph a fully assembled holder. The base plate was made of aluminum. All other parts were 3D-printed (Vero ink, on Stratasys J750). Screw size: 6/32 x ½".
The third lateral flow assay holder is used when the locations of pinch points, wells, etc, have been determined using the first or the second holder ( Figure S5). It was also made using primarily 3D printed parts (Vero ink on Stratasys J750). This holder uses the same aluminum base as the one in the second holder. The black 3D printed bottom plate also serves as the floor onto which LFAs are laid. It provides bars to serve as pinch points and rectangular features to guide the LFA into a specific location. The monolithic 3D printed top plate provides pinch points and wells, with predetermined locations specified in the CAD. Figure S5. Modeling photo (A) and photograph (B) of an assembled printable lateral flow assay (LFA) strip array holder. The top plate was printed using a clear resin. The bottom plate was printed in black. The LFA strips are arranged in an 8x2 array. Screw size: 6/32 x ½".
The last piece of hardware developed for this work was designed to hold individual LFA cassettes with defined spacing, allowing for precise delivery across a larger number of LFA devices. The cassettes are typical of the final or close to final design; they can even be commercial cassettes. This cassette holder consists of an ¼" thick aluminum sheet (40 by 48) that has two pin holes that guide the sheet into place. On top of the aluminum sheet is a piece of 1/16" clear acrylic (McMaster-Carr) that was laser cut (Universal Laser Systems ULTRA X6000) with space for 96 cassettes of a given size. The number and location of pipetting locations on this tray correspond to labware designed in the Hamilton software. In experiments comparing many different solutions of the capture reagent (e.g. the major capture molecule, the matrix, the concentration), it may be faster efficient to use the robot to spot the solutions, instead of striping multiple pieces of many pieces of nitrocellulose, which requires time for washing and loading between solutions. However, spotting solutions onto nitrocellulose is challenging because the material is fragile, and the volume is small (typically 1 μL per strip). We designed the hardware to be flat, tuned the liquid class (liquid class ivl_tip50_spot_JetEmpty in the attached ivl_liquid_classes.mdb), and adjusted the spotting height to achieve optimal spotting (1 mm above the nitrocellulose). To verify, we used the robot or a human operator to spot biotinylated mouse IgG (see protocol below), ran gold nanoparticle-streptavidin through for detection, and quantified the spot intensities (Table S4). We found that the coefficients of variation (CVs) of the robot were comparable to those of the human operator. operator ( Figure S7, Table S5). This result indicates that the robot can spot reagents consistently on nitrocellulose and can help facilitate the screening many capture reagents. o Columns: Amicon columns (Millipore UFC50596 lot R8HA79021, 0.5 mL) o Used 2 columns. Split the reaction mixture evenly into the 2 columns o Centrifuged at 14000 rcf for 10 minutes, discard the low molecular weight fractions that passed through the column o Added 400 μL of PBS to each column, centrifuged at 14000 rcf for 10 minutes o Flipped the columns, centrifuged at 1000 rcf for 2 minutes to recover the high molecular weight fraction o Added 50 μL of PBS to each column to wash and combine with the primary fraction -Estimated the concentration using UV-Vis (Nanodrop) (4.284 mg/mL) Table S4. Experimental setup of spotting verification

Strip configuration
Backing card: Diagnostic Consulting Network, 60 mm Conjugate pad: GFDX203000 (Millipore), dx=0 mm, length=23 mm Nitrocellulose: Satorius CN95, dx=16 mm, length=25 mm (overlap=7 mm) Wicking pad: CF5, dx=33 mm, length=22 mm (overlap=9 mm) Spotting 1 μL in the middle of the uncovered nitrocellulose (dx=28 mm) IgG-biotin solutions: 0.02, 0.1, 0.5 mg/mL in PBS Done by the robot or by a human operator Running 15 μL on conjugate pad, gold nanoparticle-streptavidin (Arista Biologicals, CGSTV-0600) , with OD 0.1 in PBST (PBS with Tween-20 0.05%) 60 μL of PBST Run by the robot Imaging On the robot, when dried Number of technical replicates 16, for each biotinylated IgG concentration and operator (robot or human) Figure S7. Plots showing signal of spotting verification, done by a human operator or a robot, with different concentrations biotinylated mouse IgG (0.02, 0.1, 0.5 mg/mL). Experimental details are described in Table S4 (N=16 for each box plot). Table S5. Coefficients of variation (CVs) of spot intensities resulting from spotting by a human operator and the robot (N=16).
Concentration of capture reagent (mg/mL) Human CV Robot CV 0.5 5% 9% 0.1 6% 7% 0.02 8% 10% Figure S8. Comparison of biotinylated TBL005 when biotinylated at three different ratios; 10:1, 20:1, and 40:1. Antibody pair screening used 10:1 as the biotins/Ab ratio for screening to maintain consistency across all antibodies tested. However, this data shows that the optimal ratio for this particular antibody would be closer to 20:1. This suggests that selecting one ratio for all antibodies may bias towards or away from certain antibodies and is something that must be considered during experimental design.